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AI For Medical Treatment
  • Level Professional
  • Duration 22 hours
  • Course by DeepLearning.AI
  • Offered by

About

AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Medical treatment may impact patients differently based on their existing health conditions. In this third course, you'll recommend treatments more suited to individual patients using data from randomized control trials. In the second week, you'll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, you'll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend that you take the Deep Learning Specialization.

Modules

Introduction

    • 2 Videos
    • 1 Readings
Show info about module content

2 Videos

  • Intro to Course 3 with Andrew and Pranav
  • About Course 3

1 Readings

  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!

Randomized Control Trials

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Pandas for a Medical Dataset

2 Videos

  • Absolute Risk Reduction
  • Randomized Control Trials

Average Treatment Effect

    • 5 Videos
    • 1 Readings
    • 1 Lab
Show info about module content

1 Labs

  • Model Training/Tuning Basics with Sklearn

5 Videos

  • Causal Inference
  • Average Treatment Effect
  • Conditional Average Treatment Effect
  • T-Learner
  • S-Learner

1 Readings

  • Clarifications about Upcoming Causal Inference

Individualized Treatment Effect

    • 3 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Logistic Regression Model Interpretation

3 Videos

  • Evaluate Individualized Treatment Effect
  • C-for-benefit
  • C-for-benefit Calculation

Quiz

    • 1 Assignment
Show info about module content

1 Assignment

  • Measuring Treatment Effects

Assignment: Treatment Effect Estimation

    • 2 Readings
    • 1 Programming
Show info about module content

1 Programming

  • Estimating Treatment Effect Using Machine Learning

2 Readings

  • (Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace
  • About the AutoGrader

Question Answering

    • 3 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Cleaning Text

3 Videos

  • Medical Question Answering
  • Handling Words with Multiple Meanings
  • Define the Answer in a Text

Automatic Labeling

    • 4 Videos
    • 1 Lab
Show info about module content

1 Labs

  • BioC Format and the NegBio Library

4 Videos

  • Automatic Label Extraction for Medical Imaging
  • Synonyms for Labels
  • Is-a Relationships for Labels
  • Presence or Absence of a Disease

Evaluate Automatic Labeling

    • 3 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Preparing Input for Text Classification

3 Videos

  • Evaluating Label Extraction
  • Precision, Recall and F1 Score
  • Evaluating on Multiple Disease Categories

Quiz

    • 1 Assignment
Show info about module content

1 Assignment

  • Information Extraction with NLP

Assignment: Natural Language Entity Extraction

    • 1 Programming
Show info about module content

1 Programming

  • Natural Language Entity Extraction

Feature Importance

    • 2 Videos
    • 1 Lab
Show info about module content

1 Labs

  • Permutation Method

2 Videos

  • Drop Column Method
  • Permutation Method

Individual Feature Importance

    • 4 Videos
Show info about module content

4 Videos

  • Individual Feature Importance
  • Shapley Values
  • Combining Importances
  • Shapley Values for all Patients

Interpreting Deep Learning Models

    • 3 Videos
    • 2 Lab
Show info about module content

2 Labs

  • Introduction to GradCAM (Part 1)
  • GradCAM: Continuation (Part 2)

3 Videos

  • Interpreting CNN Models
  • Localization Maps
  • Heat Maps

Quiz

    • 1 Assignment
Show info about module content

1 Assignment

  • ML Interpretation

End of access to Lab Notebooks

    • 1 Readings
Show info about module content

1 Readings

  • [IMPORTANT] Reminder about end of access to Lab Notebooks

Assignment: ML Interpretation

    • 1 Programming
Show info about module content

1 Programming

  • ML Interpretation

Acknowledgments

    • 3 Readings
Show info about module content

3 Readings

  • Acknowledgements
  • Citations
  • (Optional) Opportunity to Mentor Other Learners

Auto Summary

"AI For Medical Treatment" is a specialized course designed for professionals in Data Science & AI, offered by Coursera. The course focuses on leveraging AI to enhance medical practices, including personalized treatment recommendations, interpreting machine learning models, and automating medical data labeling. With a duration of 1320 minutes, learners gain practical experience through real-world applications. Subscription options include Starter, Professional, and Paid tiers. Ideal for those with a foundational understanding of deep learning, seeking to apply AI in medical scenarios.

Pranav Rajpurkar
Instructors

Pranav Rajpurkar

Bora Uyumazturk
Instructors

Bora Uyumazturk

Amirhossein Kiani
Instructors

Amirhossein Kiani